Supply-chain network design via simulation, optimization, and greenfield analysis.
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Average 4.6/5 across 11 of 11 tools scored. Lowest: 4/5.
Each tool has a clearly distinct role: listing, describing, explaining, getting results, or running simulation. No two tools overlap in purpose; for example, describe_* and explain_* serve different needs (precomputed detail vs. conceptual reference).
All tool names follow a consistent verb_noun pattern with underscores: describe_, explain_, get_, list_, run_. The verb indicates the action, and the noun indicates the resource, making it predictable and easy to understand.
With 11 tools, the set is well-scoped for a demonstration-focused supply chain modeling server. It covers listing, describing, explaining, retrieving results, and running a simulation without unnecessary duplication or missing essential operations.
The tool surface is comprehensive for querying and running bundled demos, but it lacks create/update/delete operations for models or demos. This is acceptable since the server provides precomputed results, but it represents a minor gap in lifecycle coverage.
describe_greenfield_demoAFull detail on one greenfield demo — region, customer count, available dc_count values, and the score-curve elbow finding. Use this before get_greenfield_result to know what dc_count values are precomputed.
| Name | Required | Description | Default |
|---|---|---|---|
| demo_id | Yes | Which greenfield demo to describe |
| Name | Required | Description |
|---|---|---|
| content | No | MCP content blocks — single text block with the response body |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, destructiveHint. The description adds value by describing the return details and its role as a prerequisite, enhancing transparency beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences: first lists what the tool provides, second gives usage guidance. No wasted words, front-loaded with key information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple schema, rich annotations, and existing output schema, the description fully covers purpose, outputs, and usage context. It is complete for an agent to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a clear parameter description for demo_id. The tool description does not add significant semantic depth beyond that, so baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it provides full detail on a greenfield demo, listing specific outputs (region, customer count, dc_count values, elbow finding). It differentiates from sibling get_greenfield_result by indicating sequencing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly tells to use this before get_greenfield_result, providing clear context. Does not explicitly exclude alternatives, but the guidance is sufficient for an agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
describe_opt_demoAFull detail on one optimization demo — controls, available scenario keys, sites, fixed parameters, citations, and the key finding the demo illustrates. Use this before get_opt_result to know what scenario_key values are accepted.
| Name | Required | Description | Default |
|---|---|---|---|
| demo_id | Yes | Which optimization demo to describe |
| Name | Required | Description |
|---|---|---|
| content | No | MCP content blocks — single text block with the response body |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds behavioral context by listing returned fields but no contradictions or hidden caveats.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences; first sentence defines purpose with specifics, second sentence gives usage guidance. No wasted words, front-loaded with key information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 1 parameter fully documented, output schema present, and annotations covering safety, the description is complete: tells what, when, and what it returns. No gaps identified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with enum and description. Description adds semantic value by linking the parameter to the next step (scenario_key acceptance), providing context beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states verb 'describe' and resource 'optimization demo', listing specific details (controls, scenario keys, sites, etc.). Distinguishes from siblings like 'get_opt_result' and 'list_opt_demos'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Use this before get_opt_result to know what scenario_key values are accepted', guiding the agent on when to use. Does not explicitly exclude other contexts, but the guidance is specific and valuable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
explain_greenfieldAReference text on greenfield analysis — clean-slate facility-location math. Covers the weighted center-of-gravity (Weber) formulation, Weiszfeld's iterative algorithm, Lloyd's-style alternating location-allocation for N facilities, service constraints (% demand vs % customers within a distance band), and the inverse problem of solving for minimum N. Also covers when to use greenfield vs facility selection (the open/close MIP). Pure static text — no engine call, deterministic output. Use this when the user asks a conceptual 'how does greenfield analysis work' or 'where would I put my DCs' question. ChiAha's GreenfieldAnalysis engine powers the US Greenfield Design demo on the sandbox.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| content | No | MCP content blocks — single text block with the response body |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description states 'Pure static text — no engine call, deterministic output,' which goes beyond annotations that already mark it as read-only and idempotent. It confirms the tool's behavior without contradiction, adding value by explaining that no external computation occurs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with the key purpose. It lists specific topics covered, which is informative but slightly verbose. Every sentence serves a purpose, though the detail level could be trimmed without losing clarity. Still, it earns its length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters, good annotations, and an output schema, the description is complete. It covers what the tool returns (conceptual text), when to use it, and even notes the supporting engine and demo. No gaps remain for an agent to understand this conceptual tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so baseline is 4. The description does not need to add parameter information, and it does not. No additional semantics are required, and the description appropriately focuses on the output content.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool provides 'reference text on greenfield analysis' and lists specific mathematical formulations and concepts it covers. It distinguishes well from sibling tools like 'get_greenfield_result' and 'describe_greenfield_demo' which serve different purposes (computation vs. demo description).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use this when the user asks a conceptual "how does greenfield analysis work" or "where would I put my DCs" question.' It also contrasts greenfield vs facility selection, providing clear decision guidance. No explicit when-not-to-use is needed as the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
explain_optimizationAReference text on supply-chain network optimization — mixed-integer programming (MIP), the structure of decision variables and constraints, the objective function for landed-cost minimization, and the common problem classes (facility selection, sourcing, flow constraints, multi-period, BOM/production, multi-objective). Also covers when to reach for optimization vs simulation. Pure static text — no engine call, deterministic output. Use this when the user asks a conceptual 'how does network optimization work' question. ChiAha's AMOS optimizer (open-source, Odin, GLOP/CBC via OR-Tools) powers the Tariff and Coffee Co-pack demos on the sandbox.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| content | No | MCP content blocks — single text block with the response body |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds 'Pure static text — no engine call, deterministic output,' which confirms no side effects and clarifies the tool's behavior beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is four sentences, front-loaded with the core purpose. Every sentence adds value: purpose, topics covered, usage note, and behavioral detail. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters and an output schema exists, the description fully covers what the tool does, what it returns, and when to use it. It mentions the content scope and the underlying engine (AMOS optimizer) for context. No gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has no parameters (schema empty, coverage 100%). As per rule, baseline is 4. The description does not need to explain parameters since there are none, and it doesn't waste space on unnecessary details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it provides 'Reference text on supply-chain network optimization' and lists specific topics (MIP, decision variables, constraints, etc.). It also explicitly says when to use: 'when the user asks a conceptual how does network optimization work question.' This distinguishes it from sibling tools like run_simulation or get_opt_result.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives a clear usage instruction: 'Use this when the user asks a conceptual... question.' It also mentions covering the distinction between optimization and simulation, implying when not to use this tool, but does not explicitly name sibling alternatives for simulation. Still, it provides good contextual guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_greenfield_resultAGet the precomputed result for one DC count of a greenfield demo. Returns sited DCs (lat/lon + city/state, nearest-city snapped), customer-to-DC assignments, and the score for that DC count. ANTI-FABRICATION: every result is verbatim engine output from greenfield-cli — quote them in your reply, do not round or fabricate cities.
| Name | Required | Description | Default |
|---|---|---|---|
| demo_id | Yes | Which greenfield demo | |
| dc_count | Yes | Number of DCs to site (2-8) |
| Name | Required | Description |
|---|---|---|
| content | No | MCP content blocks — single text block with the response body |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, covering safety and side effects. The description adds important behavioral context beyond annotations: it emphasizes that results are verbatim engine output, instructing the agent not to fabricate or round. This disclosure is valuable for proper tool use.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences plus a short emphasized instruction. It efficiently states the tool's purpose, output contents, and a critical usage note. Every sentence contributes value, with no redundant or vague phrasing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema and rich annotations, the description covers purpose, output details, and a key behavioral instruction. It is mostly complete but lacks explicit guidance on when to prefer this tool over siblings. Nonetheless, it provides sufficient context for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with both parameters having descriptive enums. The description does not add new meaning beyond the schema; it mentions 'DC count' and the return format but does not elaborate on parameter values or constraints. Given high schema coverage, a baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves a precomputed result for a specific DC count in a greenfield demo. It specifies the returned data (sited DCs with lat/lon, city/state, assignments, score), distinguishing it from sibling tools like describe_greenfield_demo or get_opt_result.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes an anti-fabrication instruction for handling results, which guides usage. However, it does not explicitly state when to use this tool versus alternatives or when not to use it. The context of sibling tools provides some differentiation, but clear exclusions are missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_opt_resultAGet the precomputed result for one scenario of an optimization demo. Returns the verbatim engine output JSON (AMOS for tariff/coffee, SSO output for sso-basic) including the optimal sourcing/production/transport decisions, costs, and any open/close facility variables. ANTI-FABRICATION: every numeric result is verbatim from the optimization engine that ran offline — quote them in your reply, do not round or recompute. Call describe_opt_demo first to learn valid scenario_key formats for each demo.
| Name | Required | Description | Default |
|---|---|---|---|
| demo_id | Yes | Which optimization demo | |
| scenario_key | No | Scenario key within the demo. Format varies per demo — call describe_opt_demo for the exact valid keys before guessing. Tariff uses 'APAC=<N>' where N is one of 0, 7.5, 25, 50, 100. Coffee uses '<configKey>|DSL=<N>' where configKey is T/TA/TS/TAS and N is 20-70 in steps of 5 (10c units of $/gal). sso-basic is single-scenario; scenario_key is ignored. |
| Name | Required | Description |
|---|---|---|
| content | No | MCP content blocks — single text block with the response body |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, non-destructive. Description adds anti-fabrication rule (do not round/recompute), verbatim return, and scenario_key behavior for sso-basic. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, each dense with information. Front-loaded with purpose. No fluff. Efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given complexity of different demo formats, presence of output schema, and rich annotations, description is fully complete. Covers variations, anti-fabrication, and prerequisite call.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but description adds critical context: scenario_key format varies per demo, gives examples, and notes scenario_key is ignored for sso-basic. Provides beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it gets precomputed result for a scenario of an optimization demo. Specifies it returns verbatim engine output, distinguishing it from sibling tools like describe_opt_demo or get_greenfield_result.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly advises to call describe_opt_demo first to learn valid scenario_key formats. Provides specific format examples for tariff, coffee, and sso-basic. Lacks explicit 'when not to use' but provides sufficient guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_sc_theoryAReference guide to supply-chain simulation concepts: ordering policies, BOM, FDD formulas, event-driven simulation. Pure static text — no engine call, deterministic output. Use this when the user asks a conceptual 'how does this work' question rather than asking for a number.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| content | No | MCP content blocks — single text block with the response body |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds 'Pure static text — no engine call, deterministic output,' reinforcing the safe, predictable behavior with no side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences: first defines the tool, second provides usage guidance. No wasted words, front-loaded with essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters, comprehensive annotations, and an output schema (exists), the description sufficiently explains the tool's purpose and behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, so schema coverage is 100%. The description does not need to add parameter info; baseline 4 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it is a reference guide to supply-chain simulation concepts, listing specific topics (ordering policies, BOM, FDD formulas, event-driven simulation). It distinguishes from sibling tools like run_simulation by noting it provides static text, not numerical output.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says to use when the user asks a conceptual 'how does this work' question rather than for a numerical result. This provides clear guidance on when to use versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_greenfield_demosAList the bundled SCModeling greenfield demos. Returns id + label + one-line summary. Currently one demo (US, 189 customer points). Use this before describe_greenfield_demo or get_greenfield_result.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| content | No | MCP content blocks — single text block with the response body |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already set readOnlyHint, idempotentHint, destructiveHint. The description adds that it returns specific fields (id, label, summary) and notes the bundled nature and current demo count. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with purpose, no redundant or extraneous information. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a parameterless list tool with output schema and clear annotations, the description is complete. It states what is returned, hints at usage order, and the simple nature means no further behavioral context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist (schema coverage 100%), so baseline is 4. The description adds context about return values (id, label, summary) beyond the empty input schema, which is valuable for agent understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('List'), clearly identifies the resource ('bundled SCModeling greenfield demos'), and specifies the return fields (id + label + one-line summary). It distinguishes itself from sibling tools like describe_greenfield_demo and list_opt_demos.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states 'Use this before describe_greenfield_demo or get_greenfield_result,' giving clear when-to-use guidance relative to sibling tools. This helps an AI agent decide tool invocation order.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_modelsAList the bundled SCModeling sample supply-chain models. Returns a catalog with each model's id and a short description. Use this before run_simulation to know which model_id values are valid.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| content | No | MCP content blocks — single text block with the response body |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, and destructiveHint. The description adds return format (catalog with id and description), which is useful but not required beyond annotations. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no wasted words. Front-loaded with action and resource.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with no parameters and an output schema, the description covers purpose, return content, and usage context. Complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters, so the description does not need to add param info. Baseline for 0 parameters is 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it lists bundled SCModeling sample supply-chain models and returns a catalog with id and description. It differentiates from sibling tools like list_greenfield_demos by specifying it's for sample supply-chain models.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says to use before run_simulation to know valid model_id values. This provides clear context, though it does not state exclusions or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_opt_demosAList the bundled SCModeling optimization demos. Returns id + label + one-line summary for each (Tariff, Coffee Co-pack, SSO Basic). Use this before describe_opt_demo or get_opt_result to know which demo_id values are valid. All demos are precomputed sample-only fixtures — for optimization on real client data, the SCModeling desktop tool is the product.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| content | No | MCP content blocks — single text block with the response body |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds that demos are precomputed sample-only fixtures, which is valuable context beyond what annotations provide. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, each earning its place: first states purpose and returns, second gives usage guidance, third sets expectations. No wasted words, front-loaded with the main action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters and the presence of an output schema, the description is complete. It explains what the tool returns, how to use it, and its limitations (sample-only). No additional information is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, and schema coverage is 100% (no params). The description does not need to add parameter information. Baseline of 4 is appropriate since it provides no additional param info but also needs none.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it lists bundled optimization demos, specifies that it returns id, label, and one-line summary, and gives examples (Tariff, Coffee Co-pack, SSO Basic). This distinguishes it from sibling tools like describe_opt_demo and get_opt_result.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly tells the agent to use this before describe_opt_demo or get_opt_result to know valid demo_id values. Also clarifies that for real client data, the SCModeling desktop tool should be used instead, providing clear when-to-use and when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
run_simulationARun a supply-chain simulation on a bundled SCModeling sample model (sdi-db). Returns metrics, inventory time-series, orders, shipments, routing and BOM. ANTI-FABRICATION: the returned numbers come from a real discrete-event simulation run on the sc-sim engine. Quote them VERBATIM in your reply. Do not round, estimate, average, or compute derived figures from training-data recall. If the user asks a follow-up about the same model, re-call this tool rather than recalling numbers from earlier in the conversation.
| Name | Required | Description | Default |
|---|---|---|---|
| model_id | Yes | Which sample model to simulate |
| Name | Required | Description |
|---|---|---|
| details | No | Full run detail: config, locations, materials, routing, demands, orders, shipments, inventory_timeseries |
| metrics | No | Top-line scalar KPIs (orders, shipments, simulation_days) |
| metadata | No | Model name, version, timestamp |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses key behavioral traits beyond annotations: it emphasizes that results come from a real simulation and warns against fabrication. Annotations already indicate readOnlyHint=true, but the description adds critical context about the authenticity and handling of output data.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded with the main purpose. It includes necessary instructions without excessive wordiness. Minor redundancy exists (e.g., 'ANTI-FABRICATION' could be more succinct) but overall well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, the description is complete: it explains what the tool does, what it returns, and how to use the output correctly (verbatim quoting, re-calling for follow-ups). The output schema exists to cover return structure, so no further detail is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides 100% coverage with a description for model_id ('Which sample model to simulate') and an enum of three options. The description does not add new information about the parameter beyond mentioning 'bundled sample model' and listing the options in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it runs a supply-chain simulation on bundled sample models and lists the outputs (metrics, time-series, etc.). It distinguishes itself from sibling tools which are for describing or explaining demos, not running simulations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear guidance on when to use the tool (to get simulation results) and includes important usage instructions like quoting verbatim and re-calling for follow-ups. However, it does not explicitly state when not to use it (e.g., if the user needs a description instead).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
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The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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